Simulating counterfactuals
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909237031993344 |
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| author | Karvanen, Juha Tikka, Santtu Vihola, Matti |
| author_facet | Karvanen, Juha Tikka, Santtu Vihola, Matti |
| contents | Counterfactual inference considers a hypothetical intervention in a parallel world that shares some evidence with the factual world. If the evidence specifies a conditional distribution on a manifold, counterfactuals may be analytically intractable. We present an algorithm for simulating values from a counterfactual distribution where conditions can be set on both discrete and continuous variables. We show that the proposed algorithm can be presented as a particle filter leading to asymptotically valid inference. The algorithm is applied to fairness analysis in credit-scoring. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_15328 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Simulating counterfactuals Karvanen, Juha Tikka, Santtu Vihola, Matti Machine Learning Computers and Society Computation Counterfactual inference considers a hypothetical intervention in a parallel world that shares some evidence with the factual world. If the evidence specifies a conditional distribution on a manifold, counterfactuals may be analytically intractable. We present an algorithm for simulating values from a counterfactual distribution where conditions can be set on both discrete and continuous variables. We show that the proposed algorithm can be presented as a particle filter leading to asymptotically valid inference. The algorithm is applied to fairness analysis in credit-scoring. |
| title | Simulating counterfactuals |
| topic | Machine Learning Computers and Society Computation |
| url | https://arxiv.org/abs/2306.15328 |